GMM Weights Adaptation Based on Subspace Approaches for Speaker Verification

In this paper, we explored the use of Gaussian Mixture Model (GMM) weights adaptation for speaker verification. We compared two different subspace weight adaptation approaches: Subspace Multinomial Model (SMM) and Non-Negative factor Analysis (NFA). Both techniques achieved similar results and seemed to outperform the retraining maximum likelihood (ML) weight adaptation. However, the training process for the NFA approach is substantially faster than the SMM technique. The i-vector fusion between each weight adaptation approach and the classical i-vector yielded slight improvements on the telephone part of the NIST 2010 Speaker Recognition Evaluation dataset.